{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多目标跟踪\n",
    "\n",
    "\n",
    "\n",
    "其中就是先定位到个人，然后对个人进行跟踪，这是来自项目：[Smorodov/Multitarget-tracker](https://github.com/Smorodov/Multitarget-tracker)的一则检测结果，看着很酷炫的样子。\n",
    "那么，其中是一种比较简单的多目标追踪方式：detector+tracker，两者其实是相对独立的。\n",
    "![在这里插入图片描述](https://img-blog.csdnimg.cn/20190119220546619.?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3NpbmF0XzI2OTE3Mzgz,size_16,color_FFFFFF,t_70)\n",
    "这是笔者画的一个草图，这里的流程就是，图片经过detector，得到人体坐标框，然后计算中心点位置centers（x0,y0），将该centers（x0,y0）输入给追踪器，追踪器去学习（Update）并给出预测。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from tracker import Tracker\n",
    "import copy\n",
    "import colorsys\n",
    "import os,sys,argparse,random\n",
    "from timeit import default_timer as timer\n",
    "import cv2\n",
    "import numpy as np\n",
    "from keras import backend as K\n",
    "from keras.models import load_model\n",
    "from keras.layers import Input\n",
    "from PIL import Image, ImageFont, ImageDraw\n",
    "\n",
    "from yolo3.model import yolo_eval, yolo_body, tiny_yolo_body\n",
    "from yolo3.utils import letterbox_image\n",
    "from keras.utils import multi_gpu_model\n",
    "from yolo_matt import YOLO, detect_video\n",
    "\n",
    "from tqdm import tqdm\n",
    "from scipy import misc\n",
    "\n",
    "%matplotlib inline \n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from objecttracker.KalmanFilterTracker import Tracker  # 加载卡尔曼滤波函数\n",
    "\n",
    "def calc_center(out_boxes,out_classes,out_scores,score_limit = 0.5):\n",
    "    outboxes_filter = []\n",
    "    for x,y,z in zip(out_boxes,out_classes,out_scores):\n",
    "        if z > score_limit:\n",
    "            if y == 0: # person标签为0\n",
    "                outboxes_filter.append(x)\n",
    "    \n",
    "    centers= []\n",
    "    number = len(outboxes_filter)\n",
    "    for box in outboxes_filter:\n",
    "        top, left, bottom, right = box\n",
    "        center=np.array([[(left+right)//2],[(top+bottom)//2]])\n",
    "        centers.append(center)\n",
    "    return centers,number\n",
    "\n",
    "\n",
    "def get_colors_for_classes(num_classes):\n",
    "    \"\"\"Return list of random colors for number of classes given.\"\"\"\n",
    "    # Use previously generated colors if num_classes is the same.\n",
    "    if (hasattr(get_colors_for_classes, \"colors\") and\n",
    "            len(get_colors_for_classes.colors) == num_classes):\n",
    "        return get_colors_for_classes.colors\n",
    "\n",
    "    hsv_tuples = [(x / num_classes, 1., 1.) for x in range(num_classes)]\n",
    "    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n",
    "    colors = list(\n",
    "        map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),\n",
    "            colors))\n",
    "    #colors = [(255,99,71) if c==(255,0,0) else c for c in colors ]  # 单独修正颜色，可去除\n",
    "    random.seed(10101)  # Fixed seed for consistent colors across runs.\n",
    "    random.shuffle(colors)  # Shuffle colors to decorrelate adjacent classes.\n",
    "    random.seed(None)  # Reset seed to default.\n",
    "    get_colors_for_classes.colors = colors  # Save colors for future calls.\n",
    "    return colors\n",
    "\n",
    "def trackerDetection(tracker,image,centers,number,max_point_distance = 30,max_colors = 20,track_id_size = 0.8):\n",
    "    '''\n",
    "        - max_point_distance为两个点之间的欧式距离不能超过30\n",
    "            - 有多条轨迹,tracker.tracks;\n",
    "            - 每条轨迹有多个点,tracker.tracks[i].trace\n",
    "        - max_colors,最大颜色数量\n",
    "        - track_id_size,每个中心点，显示的标记字体大小\n",
    "    '''\n",
    "    #track_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),\n",
    "    #            (0, 255, 255), (255, 0, 255), (255, 127, 255),\n",
    "    #            (127, 0, 255), (127, 0, 127)]\n",
    "    track_colors = get_colors_for_classes(max_colors)\n",
    "    \n",
    "    result = np.asarray(image)\n",
    "    font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "    cv2.putText(result, str(number), (20,  40), font, 1, (0, 0, 255), 5)  # 左上角，人数计数\n",
    "\n",
    "    if (len(centers) > 0):\n",
    "        # Track object using Kalman Filter\n",
    "        tracker.Update(centers)\n",
    "        # For identified object tracks draw tracking line\n",
    "        # Use various colors to indicate different track_id\n",
    "        for i in range(len(tracker.tracks)):\n",
    "            # 多个轨迹\n",
    "            if (len(tracker.tracks[i].trace) > 1):\n",
    "                x0,y0 = tracker.tracks[i].trace[-1][0][0],tracker.tracks[i].trace[-1][1][0]\n",
    "                cv2.putText(result,str(tracker.tracks[i].track_id),(int(x0),int(y0)),font,track_id_size,(255, 255, 255),4)  \n",
    "                # (image,text,(x,y),font,size,color,粗细)\n",
    "                for j in range(len(tracker.tracks[i].trace) - 1):\n",
    "                    #每条轨迹的每个点\n",
    "                    # Draw trace line\n",
    "                    x1 = tracker.tracks[i].trace[j][0][0]\n",
    "                    y1 = tracker.tracks[i].trace[j][1][0]\n",
    "                    x2 = tracker.tracks[i].trace[j + 1][0][0]\n",
    "                    y2 = tracker.tracks[i].trace[j + 1][1][0]\n",
    "                    clr = tracker.tracks[i].track_id % 9\n",
    "                    distance = ((x2 - x1)** 2 + (y2 - y1)**2)**0.5\n",
    "                    if distance <  max_point_distance:\n",
    "                        cv2.line(result, (int(x1), int(y1)), (int(x2), int(y2)),\n",
    "                                 track_colors[clr], 4)\n",
    "    return tracker,result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_data/yolo.h5 model, anchors, and classes loaded.\n"
     ]
    }
   ],
   "source": [
    "# 加载keras yolov3 voc预训练模型\n",
    "yolo_test_args = {\n",
    "    \"model_path\": 'model_data/yolo.h5',\n",
    "    \"anchors_path\": 'model_data/yolo_anchors.txt',\n",
    "    \"classes_path\": 'model_data/coco_classes.txt',\n",
    "    \"score\" : 0.3,\n",
    "    \"iou\" : 0.45,\n",
    "    \"model_image_size\" : (416, 416),\n",
    "    \"gpu_num\" : 1,\n",
    "}\n",
    "\n",
    "\n",
    "yolo_test = YOLO(**yolo_test_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(416, 416, 3)\n",
      "Found 2 boxes for img\n",
      "person 0.80 (187, 562) (248, 711)\n",
      "person 0.93 (109, 545) (183, 684)\n",
      "0.0905019361525774\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f22480a6c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "    解析方式一： 从视频保存成的图像文件中进行解析\n",
    "    先把视频-> 拆分成图像文件夹，在文件夹中逐帧解析\n",
    "'''\n",
    "\n",
    "tracker = Tracker(100, 8, 15, 100)\n",
    "#for n in tqdm(range(100)):\n",
    "image = Image.open('11.jpg')\n",
    "r_image,out_boxes, out_scores, out_classes = yolo_test.detect_image(image)\n",
    "centers,number = calc_center(out_boxes,out_classes,out_scores,score_limit = 0.5)\n",
    "tracker,result = trackerDetection(tracker,r_image,centers,number,max_point_distance = 30,max_colors = 20,track_id_size = 2)\n",
    "#misc.imsave('jpg2video/%s.jpg'%n, result)\n",
    "plt.imshow(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "    解析方式二：视频流直接解析    \n",
    "    直接读取视频流，并保存在某一个文件夹之中\n",
    "'''\n",
    "# 视频 -> 图像\n",
    "path = \"grom.mp4\"\n",
    "tracker = Tracker(100, 8, 15, 100)\n",
    "\n",
    "cap = cv2.VideoCapture(path)\n",
    "n = 0\n",
    "while(True):\n",
    "    ret, frame = cap.read()\n",
    "    if frame is None:\n",
    "        break\n",
    "    image = Image.fromarray(frame ) \n",
    "    r_image,out_boxes, out_scores, out_classes = yolo_test.detect_image(image)\n",
    "    centers,number = calc_center(out_boxes,out_classes,out_scores,score_limit = 0.6)\n",
    "    tracker,result = trackerDetection(tracker,r_image,centers,number,max_point_distance = 20)\n",
    "    #misc.imsave('%s.jpg'%n, result)\n",
    "    cv2.imwrite('%s.jpg'%n,result, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )\n",
    "    n += 1\n",
    "print('Down!')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
